flowchart LR A[Research for Ideas] --> B(Find an idea) B --> C(Non-stop writing) C --> D(Panic) D --> E(Deadline approaching) E --> |What is sleep?|F(Work the last 72 hours straight) F --> G(Submission) G --> H(Drink)
Lab 1.2 Quarto Webpage
1. Introduction
This is an introduction to the webpage. In the page, I will citate two of the most influencial papers about Gradient Boosting algorithms:
| Paper Name | Writers | Year |
|---|---|---|
| Greedy Function Approximation: A Gradient Boosting Machine | Jerome H. Friedman | 2001 |
| XGBoost: A Scalable Tree Boosting System | Chen, Tianqi and Guestrin, Carlos | 2016 |
Here’s a sentence with a footnote. 1
1.1 Gradient Boosting
**About Gradient Boosting:**
Gradient boosting of regression trees produces competitive,
highly robust, interpretable procedures for both
regression and classification.
**About XGBoost:**
In 2016 a newer scalable end-to-end tree boosting
system released, allowing data scientists to achieve
state-of-the-art results on many machine learning
challenges.
2. Boosting with XGBoost
I named my undergraduate thesis Boosting with XGBoost because I thought it was a cool name for a paper exploring the mathematics behind boosting algorithms. The most common loss function for regression problems is the MSE: \(L(y,F) = \frac{1}{2} * (y - F)^2 \text{.}\). The main reasons the MSE is common are:
- It is a smooth function.
- It is a differentiable loss function, meaning we can deply optimizing algorithms to minimize it.
The reason I chose this topic was because
Take the challenges hosted by the machine learning competition site Kaggle for example. Among the 29 challenge winning solutions 3 published at Kaggle’s blog during 2015, 17 solutions used XGBoost. Among these solutions, eight solely used XGBoost to train the model, while most others combined XGBoost with neural nets in ensembles (Chen and Guestrin 2016).
2.1 Visualizations of Writing a thesis
The best way to understand how it feels to write a thesis with time restriction, is with visualizations. The following is a good way to understand the process behind:
Another visual way to explain the proccess is with the video:
It is easy to procrastinate instead of writing too, here are memes I made instead of writing:
My Spanish friend never had a milkshake before moving to the US:
My old coach’s game day outfit during covid always reminded me of something
References
Footnotes
This is the footnote.↩︎